mlsanityData to 14 Oct 2026

pySLAM: An Open-Source, Modular, and Extensible Framework for SLAM

arXiv 2502.11955 ↗First posted 17 Feb 2025cs.RO cs.CVcited by 3

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Abstract (arXiv)

pySLAM is an open-source Python framework for Visual SLAM that supports monocular, stereo, and RGB-D camera inputs. It offers a flexible and modular interface, integrating a broad range of both classical and learning-based local features. The framework includes multiple loop closure strategies, a volumetric reconstruction pipeline, and support for depth prediction models. It also offers a comprehensive set of tools for experimenting with and evaluating visual odometry and SLAM modules. Designed for both beginners and experienced researchers, pySLAM emphasizes rapid prototyping, extensibility, and reproducibility across diverse datasets. Its modular architecture facilitates the integration of custom components and encourages research that bridges traditional and deep learning-based approaches. Community contributions are welcome, fostering collaborative development and innovation in the field of Visual SLAM. This document presents the pySLAM framework, outlining its main components, features, and usage.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 31 of its 62 references that are in the map. First posted on arXiv 17 Feb 2025.

Builds on

31 of 62 references.

References · 31

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19 more
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Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 1.

3 papers.

  1. 2025VSLAM-LAB: A Comprehensive Framework for Visual SLAM Methods and Datasetscited by 5
  2. 2026SLAM Adversarial Lab: An Extensible Framework for Visual SLAM Robustness Evaluation under Adverse Conditionscited by 1
  3. 2025Adaptive Covariance and Quaternion-Focused Hybrid Error-State EKF/UKF for Visual-Inertial Odometrycited by 0